Dataset Information

Latent Variable Poisson Models for Assessing the Regularity of Circadian Patterns over Time.

ABSTRACT: Many researchers in biology and medicine have focused on trying to understand biological rhythms and their potential impact on disease. A common biological rhythm is circadian, where the cycle repeats itself every 24 hours. However, a disturbance of the circadian pattern may be indicative of future disease. In this article, we develop new statistical methodology for assessing the degree of disturbance or irregularity in a circadian pattern for count sequences that are observed over time in a population of individuals. We develop a latent variable Poisson modeling approach with both circadian and stochastic short-term trend (autoregressive latent process) components that allow for individual variation in the degree of each component. A parameterization is proposed for modeling covariate dependence on the proportion of these two model components across individuals. In addition, we incorporate covariate dependence in the overall mean, the magnitude of the trend, and the phase-shift of the circadian pattern. Innovative Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. Several variations of the proposed models are considered and compared using the deviance information criterion. We illustrate this methodology with longitudinal physical activity count data measured in a longitudinal cohort of adolescents.

SUBMITTER: Kim S

PROVIDER: S-EPMC6447315 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Latent Variable Poisson Models for Assessing the Regularity of Circadian Patterns over Time.

Kim Sungduk S Albert Paul S PS

Journal of the American Statistical Association 20180628 523

Many researchers in biology and medicine have focused on trying to understand biological rhythms and their potential impact on disease. A common biological rhythm is circadian, where the cycle repeats itself every 24 hours. However, a disturbance of the circadian pattern may be indicative of future disease. In this article, we develop new statistical methodology for assessing the degree of disturbance or irregularity in a circadian pattern for count sequences that are observed over time in a pop ...[more]

PMID: 30956371

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Project description:BACKGROUND:Evidence suggests that the single-disease paradigm does not accurately reflect the individual experience, with increasing prevalence of chronic disease multimorbidity, and subtle yet important differences in types of co-occurring diseases. Knowledge of multimorbidity patterns can aid clarification of individual-level burden and needs, to inform prevention and treatment strategies. This study aimed to estimate the prevalence of multimorbidity in Jamaica, identify population subgroups with similar and distinct disease profiles, and examine consistency in patterns identified across statistical techniques. METHODS:Latent class analysis (LCA) was used to examine multimorbidity patterns in a sample of 2,551 respondents aged 15-74 years, based on data from the nationally representative Jamaica Health and Lifestyle Survey 2007/2008 and self-reported presence/absence of 11 chronic conditions. Secondary analyses compared results with patterns identified using exploratory factor analysis (EFA). RESULTS:Nearly one-quarter of the sample (24.1%) were multimorbid (i.e. had ?2 diseases), with significantly higher burden in females compared to males (31.6% vs. 16.1%; p<0.001). LCA revealed four distinct classes, including a predominant Relatively Healthy class, comprising 52.7% of the sample, with little to no morbidity. The remaining three classes were characterized by varying degrees and patterns of multimorbidity and labelled Metabolic (30.9%), Vascular-Inflammatory (12.2%), and Respiratory (4.2%). Four diseases determined using physical assessments (obesity, hypertension, diabetes, hypercholesterolemia) were primary contributors to multimorbidity patterns overall. EFA identified three patterns described as "Vascular" (hypertension, obesity, hypercholesterolemia, diabetes, stroke); "Respiratory" (asthma, COPD); and "Cardio-Mental-Articular" (cardiovascular disease, arthritis, mental disorders). CONCLUSION:This first study of multimorbidity in the Caribbean has revealed a high burden of co-existing conditions in the Jamaican population, that is predominantly borne by females. Consistency across methods supports the validity of patterns identified. Future research into the causes and consequences of multimorbidity patterns can guide development of clinical and public health strategies that allow for targeted prevention and intervention.

Dataset Information

Latent Variable Poisson Models for Assessing the Regularity of Circadian Patterns over Time.

Publications

Latent Variable Poisson Models for Assessing the Regularity of Circadian Patterns over Time.

Similar Datasets

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